Scholar Dropout Analysis in Technological Universities Subsystem Using Neural Network-Based Models: UT-Puebla Case
摘要
Scholar dropout is a phenomenon that affects many Higher Education Institutions in Mexico. Economic problems and failure are the main causes of this problem; however, through traditional machine learning techniques, many authors have approached this study. In this paper, the study of scholar dropout in Technological University in Puebla will be approached using deep learning techniques that are commonly used in the field of image classification and natural language processing. In particular, deep neural network techniques such as convolutional neural networks are considered, performing a treatment of the data set used to emulate the data structures required by these types of algorithm. Finally, an analysis of the performance of these models and their relevance in the study of school dropout is carried out.